A Linear Spline Markov Approximation Method for Random Maps with Position Dependent Probabilities
Mathematics and Natural Sciences
We present a rigorous convergence analysis of a linear spline Markov finite approximation method for computing stationary densities of random maps with position dependent probabilities, which consist of several chaotic maps. The whole analysis is based on a new Lasota–Yorke-type inequality for the Markov operator associated with the random map, which is better than the previous one in the literature and much simpler to obtain. We also present numerical results to support our theoretical analysis.
International Journal of Bifurcation and Chaos
(2020). A Linear Spline Markov Approximation Method for Random Maps with Position Dependent Probabilities. International Journal of Bifurcation and Chaos, 30(3).
Available at: https://aquila.usm.edu/fac_pubs/17311